4 Genomic data

4.1 Read duplicates

all_data %>%
    select(dataset,Extraction,duplicates,Taxon) %>%
    unique() %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(duplicates), sd(duplicates))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of fraction of duplicated reads")
tinytable_7c0az2p7y96m2rjip6xl
Mean and standard deviation of fraction of duplicated reads
Taxon DREX EHEX ZYMO
Amphibian 0.2±0.2 0.2±0.2 0.3±0.2
Bird 0.9±0.1 0.6±0.4 0.8±0.3
Control 0.9±0.0 1.0±0.0 1.0±0.0
Mammal 0.2±0.1 0.2±0.2 0.4±0.4
Reptile 0.3±0.3 0.4±0.4 0.5±0.4
all_data %>%
    select(dataset,Extraction,duplicates,Taxon) %>%
    unique() %>%
    mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
    mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
    ggplot(aes(x=Extraction,y=duplicates))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="Duplication rate",x="Extraction method")

all_data %>%
    select(dataset,Sample,Species,Extraction,duplicates,Taxon) %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(duplicates ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()
tinytable_171zculjd8c8psm2h61h
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 0.39361067 0.07257915 5.423192 14.33359 8.267782e-05
fixed NA ExtractionEHEX -0.05339005 0.03918499 -1.362513 160.73104 1.749423e-01
fixed NA ExtractionZYMO 0.09279626 0.03900562 2.379048 160.72377 1.853073e-02
ran_pars Sample sd__(Intercept) 0.00000000 NA NA NA NA
ran_pars Species sd__(Intercept) 0.23148121 NA NA NA NA
ran_pars Residual sd__Observation 0.21005171 NA NA NA NA

4.2 Depth of coverage

all_data %>%
    select(dataset,Extraction,coverage_depth,Taxon) %>%
    unique() %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(coverage_depth), sd(coverage_depth))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of fraction of duplicated reads")
tinytable_cj4ehdohrsqvx8h9nn8x
Mean and standard deviation of fraction of duplicated reads
Taxon DREX EHEX ZYMO
Amphibian 0.0±0.0 0.0±0.0 0.0±0.0
Bird 0.6±0.5 0.8±0.8 0.4±0.8
Control 0.0±0.0 0.0±0.0 0.0±0.0
Mammal 0.3±0.4 0.5±1.0 0.7±1.2
Reptile 0.1±0.1 0.1±0.1 0.2±0.3
all_data %>%
    select(dataset,Extraction,coverage_depth,Taxon) %>%
    unique() %>%
    mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
    mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
    ggplot(aes(x=Extraction,y=coverage_depth))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="Depth of coverage",x="Extraction method")

all_data %>%
    select(dataset,Sample,Species,Extraction,coverage_depth,Taxon) %>%
    unique() %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(coverage_depth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()
tinytable_uezua2r41tz836tqbm7y
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 0.23933333 0.1331063 1.7980622 20.58494 0.0868510
fixed NA ExtractionEHEX 0.08700000 0.1144640 0.7600640 48.00000 0.4509335
fixed NA ExtractionZYMO 0.08979167 0.1144640 0.7844531 48.00000 0.4366287
ran_pars Sample sd__(Intercept) 0.40863458 NA NA NA NA
ran_pars Species sd__(Intercept) 0.22473121 NA NA NA NA
ran_pars Residual sd__Observation 0.39651507 NA NA NA NA

4.3 Breadth of coverage

all_data %>%
    select(dataset,Extraction,coverage_breadth,Taxon) %>%
    unique() %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(coverage_breadth), sd(coverage_breadth))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of depth of host genome coverage")
tinytable_h4kmi71tafw9a17ypn4b
Mean and standard deviation of depth of host genome coverage
Taxon DREX EHEX ZYMO
Amphibian 0.0±0.0 0.0±0.0 0.0±0.0
Bird 3.2±4.4 8.9±13.9 0.6±0.5
Control 0.0±0.0 0.0±0.0 0.0±0.0
Mammal 10.2±16.4 15.1±26.4 5.7±5.9
Reptile 3.0±5.4 2.9±3.7 4.9±7.5
all_data %>%
    select(dataset,Extraction,coverage_breadth,Taxon) %>%
    unique() %>%
    mutate(Taxon=factor(Taxon,levels=c("Amphibian","Reptile","Mammal","Bird","Control"))) %>%
    mutate(Extraction=factor(Extraction,levels=c("ZYMO","DREX","EHEX"))) %>%
    ggplot(aes(x=Extraction,y=coverage_breadth))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="Breadth of coverage",x="Extraction method")

all_data %>%
    select(dataset,Extraction,Sample,Species,coverage_breadth,Taxon) %>%
    unique() %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(coverage_breadth ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()
tinytable_aqgng9yy5dvdrgm75m4y
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 4.100417 2.252826 1.8201211 20.82558 0.08314479
fixed NA ExtractionEHEX 2.617500 1.956426 1.3378986 48.00000 0.18723379
fixed NA ExtractionZYMO -1.301250 1.956426 -0.6651158 48.00000 0.50915975
ran_pars Sample sd__(Intercept) 7.118462 NA NA NA NA
ran_pars Species sd__(Intercept) 3.549767 NA NA NA NA
ran_pars Residual sd__Observation 6.777259 NA NA NA NA